Effect of Aggregation Kinetics on the Thermal Conductivity of Nanoscale Colloidal Solutions (Nanofluid)

Prasher, Ravi; Phelan, Patrick E.; Bhattacharya, Pushpak

doi:10.1021/nl060992s

Cited by 620 publications

(409 citation statements)

References 30 publications

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“…It is observed that heat conductivity first increases linearly with the degree of agglomeration, reaches a maximum at the percolation threshold and finally decreases. The raising of conductivity with size (at nanoscale) was confirmed by many authors [46][47][48][49][50][51] in the case of epoxy resin with various fillers and by Prasher et al [31,52] in the case of nanofluids.…”

Section: Final Validation Of Dependence Of the Effective Thermal Condmentioning

confidence: 70%

Effect of volume-fraction dependent agglomeration of nanoparticles on the thermal conductivity of nanocomposites: Applications to epoxy resins, filled by SiO2, AlN and MgO nanoparticles

Machrafi

Lebon

Iorio

2016

Composites Science and Technology

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Abstract.A thermodynamic model for transient heat conduction in ceramic-polymer nanocomposites is proposed. The model takes into account particle's size, particle's volume fraction, and interface characteristics with emphasis on the effect of agglomeration of particles on the effective thermal conductivity of the nanocomposite.The originality of the present work is its basement on extended irreversible thermodynamics, combining nano-and continuum-scales without invoking molecular dynamics. The model is compared to experimental data using the examples of SiO2, AlN and MgO nanoparticles embedded in epoxy resin. The analysis is limited to spherical nanoparticles. The dependence of the degree of agglomeration with respect of the volume fraction of particles is also discussed and a power-law relation is established through a kinetic mechanism and experiments performed in our laboratory. This relation is used in 2 our theoretical model, resulting into a good agreement with experiments. It is shown that the effective thermal conductivity may either increase or decrease with the degree of agglomeration.

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Section: Final Validation Of Dependence Of the Effective Thermal Condmentioning

confidence: 70%

Effect of volume-fraction dependent agglomeration of nanoparticles on the thermal conductivity of nanocomposites: Applications to epoxy resins, filled by SiO2, AlN and MgO nanoparticles

Machrafi

Lebon

Iorio

2016

Composites Science and Technology

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“…The second mechanism stimulates phonons transference along large particles or particle aggregates, which involves that particles with large aspect ratio have better heat transfer properties Özerinç et al, 2010). As a result, the size and shape of nanoparticles and clusters formed are the key factors for the thermal conductivity enhancement Prasher et al, 2006;Shima et al, 2010;Warrier et al, 2010;Wu et al, 2010). It was demonstrated that chain-like structures and nanotubes or nanofibers provide the highest thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

Characterization of halloysite-water nanofluid for heat transfer applications

Alberola

Mondragón

Juliá

et al. 2014

Applied Clay Science

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Versión / Versió:Postprint Nanofluids based on water with halloysite (Hal) nanotubes were prepared and characterized in order to evaluate its suitability to be used as a heat transfer fluid. A characterization of the Hal powder nanoparticles was performed by means of SEM, TEM, WAXS, FTIR and TGA so that chemical composition, size and shape were determined. Stability of nanofluids was analyzed by means of zeta potential and light transmission measurements. Thermal conductivity, specific heat and viscosity of nanofluids prepared at different solid contents (0.5, 1, 3 and 5% volume fraction) and temperatures (40, 60 and 80°C) were obtained in order to optimize the Prandtl number. The nanofluids exhibited a good performance for its application as heat transfer fluids, with low Prandtl numbers compared to other commonly used nanofluids. High thermal conductivity enhancement with moderate viscosity and good stability results was obtained for the Hal nanofluid.

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“…An interesting phenomenon is that most of the viscosities are under-predicted by the traditional Einstein viscosity model [9,10]. This discrepancy could be caused by aggregation of the nanoparticles and an underestimated effective volume fraction of clusters [10,11]. Rubio-Hernandez et al…”

mentioning

confidence: 99%

“…Rooij et al [12] used the fractal model to analyze aggregation in a polymer dispersion and studied the shear viscosity. Prasher et al [10,11] and Jiang et al [13] studied the kinetic process of nanoparticle aggregation, in which fractal models were used to describe the structures of nanoparticle aggregates and were combined with the effective medium theory to predict their physical properties. For the kinetic model study, the formation process and structure of an aggregate are related to particle-particle interactions.…”

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confidence: 99%

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Viscosity and aggregation structure of nanocolloidal dispersions

Wang

Luo

et al. 2012

Chin. Sci. Bull.

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In this work, the effects of nanoparticle size, particle volume fraction and pH on the viscosity of silicon dioxide nanocolloidal dispersions are investigated. Both size and pH are found to significantly affect nanocolloid viscosity. Two models are used to study the effect of aggregate structure on the viscosity of the nanocolloidal dispersion. The fractal concept is introduced to describe the irregular and dynamic aggregate structure. The structure of aggregates, which is considered to play an important role in viscosity, is affected by both intermolecular and electrostatic forces. The particle interaction is primarily affected by particle distance and becomes stronger with decreasing particle size and increasing volume fraction. The aggregate structure is also affected by the pH of the solution. Studying the relationship between pH and zeta-potential shows that with the neutralization of charges on the particle surface and decreasing electrical repulsion force, the particle interaction becomes dominated by attractive forces and the aggregates form a more compact structure. In recent years, nanocolloidal dispersions, which are heterogeneous mixtures consisting of very small particles with sizes typically in the order of 1-1000 nm, have attracted much attention for applications related to cooling [1], nanolubricants [2], drug delivery and diagnosis [3]. In the application of nanocolloidal dispersions, the viscosity, which is related to the required pumping power and diffusion properties, plays an important role in delivery systems. The rheological behavior of nanoparticle-fluid mixtures have been investigated experimentally [4][5][6][7]. Newtonian behavior has been observed for very dilute suspensions [4,7] and a considerable amount of effort has been devoted to determining the relationship between the viscosity and volume fraction of suspensions. Yu et al.[8] observed shear-thinning behavior when the volume concentration was higher than 0.03. An interesting phenomenon is that most of the viscosities are under-predicted by the traditional Einstein viscosity model [9,10]. This discrepancy could be caused by aggregation of the nanoparticles and an underestimated effective volume fraction of clusters [10,11]. Rubio-Hernandez et al.[6] also noted that the intrinsic viscosity will be affected by the shape of aggregates, which could be changed by the pH and shear rate of a colloidal system. The formation of aggregates can greatly change the transfer of heat and stress in a system because of their porous structure and large effective volume fraction. Particularly for nanocolloidal dispersions, the distance between aggregates can be in the order of several nanometers. The aggregation of nanoparticles can simply be characterized as ellipsoidal with a certain effective size and axial ratio. The size and shape of aggregates can be determined from dynamic scattering light measurements and intrinsic viscosity data, respectively [6]. Aggregation is a dynamic process and the structure changes continuously because of Brownian m...

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Effect of Aggregation Kinetics on the Thermal Conductivity of Nanoscale Colloidal Solutions (Nanofluid)

Cited by 620 publications

References 30 publications

Effect of volume-fraction dependent agglomeration of nanoparticles on the thermal conductivity of nanocomposites: Applications to epoxy resins, filled by SiO2, AlN and MgO nanoparticles

Effect of volume-fraction dependent agglomeration of nanoparticles on the thermal conductivity of nanocomposites: Applications to epoxy resins, filled by SiO2, AlN and MgO nanoparticles

Characterization of halloysite-water nanofluid for heat transfer applications

Viscosity and aggregation structure of nanocolloidal dispersions

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